text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from auto_scan_test import PassAutoScanTest, IgnoreReasons
  from program_config import TensorConfig, ProgramConfig, OpConfig
  import numpy as np
  import paddle.inference as paddle_infer
  from functools import partial
  from typing import Optional, List, Callable, Dict, Any, Set
  import unittest
  
  import hypothesis
  from hypothesis import given, settings, seed, example, assume, reproduce_failure
  import hypothesis.strategies as st
  
  
  class TestReshape2MatmulFusePass(PassAutoScanTest):
      """
          x_var  
            |          
         reshape2 
            \
      reshape2_out_var    y_var
               \           /
                   matmul      bias_var
                      \          /
                     elementwise_add  
      """
  
      def sample_predictor_configs(self, program_config):
          # TRT
          config = self.create_trt_inference_config()
          config.enable_tensorrt_engine(
              max_batch_size=10,
              workspace_size=102400,
              min_subgraph_size=0,
              precision_mode=paddle_infer.PrecisionType.Float32,
              use_static=False,
              use_calib_mode=False)
          yield config, ['mul', 'elementwise_add'], (1e-4, 1e-1)
  
      def add_ignore_pass_case(self):
          # Here we put some skip rules to avoid known bugs
          def teller1(program_config, predictor_config):
              y_shape = list(program_config.weights["matmul_y"].shape)
              bias_shape = program_config.weights["bias"].shape
              axis = program_config.ops[2].attrs["axis"]
              # bias should be [mul_y_shape[-1]]
              if axis == 0 or bias_shape[0] != y_shape[1] or len(bias_shape) != 1:
                  return True
              return False
  
          self.add_ignore_check_case(
              teller1,
              IgnoreReasons.PASS_ACCURACY_ERROR,
              "The pass error on TRT while shape of bias is not [out_size].",
          )
  
      def sample_program_config(self, draw):
          # 1. Generate shape and attr of reshape2
          reshape = draw(
              st.lists(st.integers(min_value=1, max_value=10),
                       min_size=2,
                       max_size=2))
          x_shape = reshape + [1, 1]
  
          # 2. Generate attr:transpose_X/transpose_Y/alpha of matmul
          alpha = 1.0
          transpose_X = False
          transpose_Y = False
  
          # 3. Generate legal shape of input:Y of matmul
          y_shape = draw(
              st.lists(st.integers(min_value=1, max_value=8),
                       min_size=2,
                       max_size=2))
          y_shape[0] = x_shape[1]
  
          # 4. Generate legal attr:axis of elementwise_add
          axis = draw(st.integers(min_value=-1, max_value=1))
          if axis == 0:
              axis = -1
          bias_shape = [
              y_shape[1],
          ]
          # if axis == -1:
          #     if draw(st.booleans()):
          #         bias_shape = [y_shape[1], ]
          #     else:
          #         bias_shape = [x_shape[0], y_shape[1]]
  
          reshape2_op = OpConfig(
              "reshape2",
              inputs={
                  "X": ["reshape2_x"],
              },
              shape=reshape,
              outputs={
                  "Out": ["reshape2_out"],
                  "XShape": ["xshape"]
              },
          )
          matmul_op = OpConfig(
              "matmul",
              inputs={
                  "X": ["reshape2_out"],
                  "Y": ["matmul_y"]
              },
              outputs={"Out": ["matmul_out"]},
              alpha=alpha,
              transpose_X=transpose_X,
              transpose_Y=transpose_Y,
              fused_reshape_X=[],
              fused_reshape_Y=[],
              fused_transpose_X=[],
              fused_transpose_Y=[],
              fused_reshape_Out=[],
              fused_transpose_Out=[],
          )
  
          add_op = OpConfig(
              "elementwise_add",
              inputs={
                  "X": ["matmul_out"],
                  "Y": ["bias"]
              },
              outputs={"Out": ["add_out"]},
              axis=axis,
          )
  
          ops = [reshape2_op, matmul_op, add_op]
  
          program_config = ProgramConfig(
              ops=ops,
              weights={
                  "matmul_y": TensorConfig(shape=y_shape),
                  "bias": TensorConfig(shape=bias_shape),
              },
              inputs={
                  "reshape2_x": TensorConfig(shape=x_shape),
              },
              outputs=ops[-1].outputs["Out"],
          )
  
          return program_config
  
      def test(self):
          self.run_and_statis(quant=False,
                              max_examples=50,
                              passes=["trt_reshape2_matmul_fuse_pass"])
  
  
  if __name__ == "__main__":
      unittest.main()
  